# -*- coding: utf-8 -*-
"""
# 环境:tensorflow 0.12/python3.6.1
# 作者: 王磊
"""
import tensorflow as tf
import numpy as np
"""
#=== 真实数据 集合
"""
x_data = np.linspace(-1,1,300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise# 一元二次曲线
"""
#=== 输入数据 监督学习 训练数据
"""
xs = tf.placeholder(tf.float32, [None, 1])#None:样本个数随意
ys = tf.placeholder(tf.float32, [None, 1])#None:样本个数随意
"""
#=== 函数:添加神经网络层
#=== 函参:
inputs输入数据
up_size上一层神经元结点个数
current_size当前层神经元结点个数
activation_function激活函数
"""
def add_layer(inputs, up_size, current_size, activation_function=None):
# 权值
Weights = tf.Variable(tf.random_normal([up_size, current_size]))
# 偏置
biases = tf.Variable(tf.zeros([1, current_size]) + 0.1)
# 加权和
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
# 线性输出
outputs = Wx_plus_b
else:
# 非线性输出(激活函数)
outputs = activation_function(Wx_plus_b)
return outputs
"""
#=== 搭建神经网络
"""
# 添加隐含层
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# 添加输出层
prediction = add_layer(l1, 10, 1, activation_function=None)
"""
#=== 优化神经网络
"""
# 损失函数(MSE均方误差)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))
# 梯度下降
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
"""
#=== 初始化神经网络
"""
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
"""
#=== 训练神经网络
"""
for i in range(1000):
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 10 == 0:
print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
tensorflow之搭建神经网络
最新推荐文章于 2025-01-01 06:50:08 发布